The present invention relates to a processor for use in a signal processing system and, more particularly, to a neural-based signal processor.
Neural signal processors and networks may be utilized to make more intelligent computers and to gain new insights into how the brain functions and how people think.
Acoustic emission (hereinafter referred to as AE) signals are the transient elastic waves accompanying the sudden, localized change of stress or strain in a material. These signals result when processes, such as the formation and growth of cracks, or other inelastic deformations, phase transformations, corrosion, or the like, occur in a material. The elastic waves propagate through the medium to the surface of the specimen where they can be detected by one or more sensors.
Reliable systems for the sensing and processing of AE signals have long been sought to detect and to monitor sources of emission in a structure. The emphasis of recent research on quantitative AE techniques has been toward the development of signal processing procedures by which the detected AE signals can be analyzed to recover the characteristics of the source of emission, the properties of the propagating medium or the characteristics of the sensor with its auxiliary electronics. W. Sachse, "Applications of Quantitative AE Methods: Dynamic Fracture, Materials and Transducer Characterization", published in "Solid Mechanics Research for Quantitative Non-destructive Evaluation", J. D. Achenbach and Y. Rajapakse, Eds., Matrinus Nijhoff Publishers, Dordrecht (1987), pp. 41-64.
The focus of the processor of the invention is on the source of emission. The characteristics of an AE source refer to its location and its type. The source type is generally specified in terms of the force vector or moment tensor components modeling the source, their strength, and their temporal characteristics.
The principles by which a source of AE can be located in a structure are well established, as described in "Acoustic Emission Handbook", Vol. 5 of "Non-destructive Testing Handbook", ASNT, Columbus, Ohio (1988); Y. H. Pao, "Theory of Acoustic Emission", in "Elastic Waves and Non-destructive Testing of Materials", Y. H. Pao, Ed., AMD-Vol. 29, Am. Soc. Mech. Engrs., New York (1987), pp. 107-128; and W. Sachse and Y. H. Pao, "Locating and Characterizing Sources of Acoustic Emission", in "Quant. NDE in the Nuclear Industry", R. B. Clough, Ed., Am. Soc. Metals, Metals Park, Ohio (1982), pp. 326-331. Most common are methods based on triangulation techniques. The difference in arrival times of particular wave modes, whose speed of propagation is known, is measured from the signals detected by sensors of an array. The spatial coordinates of the source may then be recovered, provided that the number of sensors exceeds the number of unknown source coordinates by at least one and that the transducers comprising the array are not in redundant positions. With a larger number of sensors, least-squares or other optimization techniques may be applied to determine the source location which best fits the arrival time data.
An alternative to the pulse arrival measurement utilizes the arrival times of two dominant pulses in the detected signals. In that case, only three signals are required to locate a source of emission in three dimensions. A small array, for which the source is always exterior to the array, utilizes both of the aforementioned procedures to locate the source of emission. U.S. Pat. No. 4,592,034 issue May, 1986 to W. Sachse and S. Sancar for "Acoustic Emission Source Location on Plate-like Structures Using a Small Array of Sensors".
The source location procedures hereinbefore described have been demonstrated with flat plate-like specimens, cylindrical pipes and various vessels, as shown in the "Acoustic Emission Handbook" and said U.S. patent. Critical to their success is an unambiguous identification of specific wave arrivals in the detected signals. This is best facilitated from knowledge of the waveform corresponding to a particular source/receiver configuration.
In order to recover the temporal characteristics as well as the vector or moment tensor components or the spatial distribution of a distributed source, a solution to the inverse source problem must be found. This requires that the effects of the specimen boundaries and the effects of the sensor and auxiliary electronics must be deconvolved from the detected displacement signals. W. Sachse, "Applications of Quantitative AE Methods . . . "; Y. H. Pao, "Theory of Acoustic Emission Techniques", Chapter 4, in "Research Techniques in Non-destructive Testing", Vol. VIII, R. S. Sharpe, Ed., Academic Press, London (1985), pp. 141-210; and K. Y. Kim and W. Sachse, "Characteristics of an Acoustic Emission Source From a Thermal Crack in Glass", Intl. J. Fracture, 21, 211-231 (1986).
The displacement signal component u.sub.i detected at a receiver location, r, in a structure from an arbitrary point source f(r',t) located at r' having source volume V.sub.o can be written as: EQU u.sub.i (r,t)=.intg..sub.V.sbsb.o f.sub.j (r',t)*G.sub.ji (r.vertline.r',t)dV' (1)
where the term G.sub.ji represents the dynamic Green's function of the structure and the asterisk denotes a time-domain convolution. K. Aki and P. G. Richards, "Quantitative Seismology: Theory and Methods", Vol. 1, Freeman, San Francisco (1980), Chapter 3.
To obtain a solution to the inverse source problem, three requirements must be met.
(1) It is necessary that the measurements be made with a receiver whose temporal and spatial transfer characteristics are known.
(2) The dynamic Green's functions appearing in Eq. (1) are available for any particular source and source/receiver geometry.
(3) Robust signal processing algorithms are used to invert Eq. (1).
To obtain the Green's functions requires either a calibration experiment with a source of identical type as will be characterized or the solution to the elastodynamic problem of a particular source emitting in a bounded elastic medium. The first approach is often difficult or may even be impossible to carry out in most experimental situations while the second is computationally intensive for real sources operating in a real material.
Heretofore, elastodynamic calculations have been restricted to signals from a point or an extended source of arbitrary type in its near-field and operating in materials which are homogeneous, isotropic, elastic and non-attenuative. Y. H. Pao, "Theory of Acoustic Emission" and A. N. Ceranoglu and Y. H. Pao, "Propagation of Elastic Pulses and Acoustic Emission in a Plate: Part I. Theory; Part II. Epicentral Response; Part III. General Responses", ASME J. Appl. Mech., 48, 125-147 (1981).
The processing algorithms by which Eq. (1) can be inverted include least-squares schemes, such as the conjugate gradient and the singular value decomposition methods. For multi-component sources, algebraic procedures, evaluation of the radiation pattern of the emitted waves, time-domain double-iterative and frequency division methods have been used. W. Sachse, C. Chang and K. Y. Kim, "Processing of AE Signals from Point and Extended Sources", in "Proceedings of the IEEE Utrasonics Symposium", IEEE, New York (1984), pp. 933-937; K. Y. Kim and W. Sachse, "Characteristics of an Acoustic Emission Source from a Thermal Crack in Glass"; and C. P. Chen, P. N. Hsieh and W. Sachse, "Signal Processing Algorithms for AE Source Characterization", which is in preparation.
It is clear from the foregoing discussion that a rigorous solution of the inverse problem based on an elastodynamic theory represents a serious obstacle for further progress in the application of quantitative analyses of acoustic emission waveforms. It is therefore advantageous to avoid, as far as is possible, this rigorous approach for characterizing a source of emission in a material. This is especially important for field or in-service applications.
One alternative to the aforementioned deterministic procedure relies on the so-called artificial intelligence and pattern recognition approaches. D. R. Hay, R. W. Y. Chan, D. Sharp and K. J. Siddigni, "Classification of Acoustic Emission Signals from Deformation Mechanisms in Aluminum Alloys", J. Acoustic Emission, 3, 118-129 (1984). In these, a system is trained by transforming the signals into an arbitrarily chosen descriptor or feature space whose components are empirically related to specific features of the AE source to be characterized. The difficulty of this approach is that it is not based on any elastodynamic theory and hence it does not enable a quantitative evaluation of an AE source in terms of variables characterizing the physical processes involved.
Systems that have previously been invented for visual and audio pattern recognition applications are described in U.S. patents issued to F. Rosenblatt (U.S. Pat. No. 3,287,649); and L. Cooper and C. Elbaum (U.S. Pat. Nos. 3,950,733; 4,254,474; and 4,326,259). Such systems may learn and may classify patterns, but they cannot be used to solve forward and inverse problems related to wave phenomena. These problems are essentially linear. Therefore, the aforementioned systems, with threshold elements at their outputs (which results in a non-linear operation), cannot be applied in a straightforward manner. Furthermore, the learning algorithm described in the aforementioned patents (Cooper et al) do not lead to a convergent learning of waveforms.
The principal object of the invention is to provide an alternate means by which the aforedescribed problems can be circumvented.
An object of the invention is to provide a neural-like signal processing system which utilizes an approximate method and which resembles the acoustic signal analysis procedure used by primitive intelligent beings. T. Kohonen, "Self-organization and Associative Memory", Springer Verlag, New York (1984) and M. A. Arbib, "Brains, Machines and Mathematics", Springer Verlag, New York (1987).
Another object of the invention is to provide a neural signal processor which functions efficiently, effectively and reliably as an intelligent processing system.
Still another object of the invention is to provide a neural signal processor suitable for use in a laboratory or a personal computer system.
Yet another object of the invention is to provide a neural signal processor which is completely independent of any elastodynamic theory, although it is capable of yielding quantitative results.
Another object of the invention is to provide a neural signal processor which utilizes the detailed features of a detected signal and wherein the entire procedure depends only on that information presented to the system during the learning process.
Still another object of the invention is to provide a neural signal processor which may form the basis for new instrumentation having application to many other intelligent non-destructive materials test and monitoring systems, as well as to systems with medical and seismological applications.